35 lines
1.4 KiB
Matlab
35 lines
1.4 KiB
Matlab
function Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,Model)
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isqdiag = all(all(abs(Q-diag(diag(Q)))<1e-14)); % ie, the covariance matrix is diagonal...
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Qvec=repmat(Q,[1 1 smpl+1]);
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for k=1:smpl
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inx = ~isnan(Model.heteroskedastic_shocks.Qvalue(:,k));
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if any(inx)
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if isqdiag
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Qvec(inx,inx,k)=diag(Model.heteroskedastic_shocks.Qvalue(inx,k));
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else
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inx = find(inx);
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for s=1:length(inx)
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if Q(inx(s),inx(s))>1.e-14
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tmpscale = sqrt(Model.heteroskedastic_shocks.Qvalue(inx(s),k)./Q(inx(s),inx(s)));
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Qvec(inx(s),:,k) = Qvec(inx(s),:,k).*tmpscale;
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Qvec(:,inx(s),k) = Qvec(:,inx(s),k).*tmpscale;
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else
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Qvec(inx(s),inx(s),k)=Model.heteroskedastic_shocks.Qvalue(inx(s),k);
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end
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end
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end
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end
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inx = ~isnan(Model.heteroskedastic_shocks.Qscale(:,k));
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if any(inx)
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if isqdiag
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Qvec(inx,inx,k)=Qvec(inx,inx,k).*diag(Model.heteroskedastic_shocks.Qscale(inx,k));
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else
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inx = find(inx);
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for s=1:length(inx)
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tmpscale = sqrt(Model.heteroskedastic_shocks.Qscale(inx(s),k));
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Qvec(inx(s),:,k) = Qvec(inx(s),:,k).*tmpscale;
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Qvec(:,inx(s),k) = Qvec(:,inx(s),k).*tmpscale;
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end
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end
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end
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end |